Abstract
Content-based image retrieval plays a key role in many domains and the volume of the image databases increases tremendously; it is very difficult to compare query image feature with every image in the dataset during the retrieval phase. Hence, search space and computational complexity increase which degrades the performance of recognition accuracy. This system investigates various search space reduction techniques, which partition or classify the image collection into a subset of related images. This study proposes an image clustering using the hybrid K-means moth flame optimization algorithm (KMFO). It enhances the performance of the K-means algorithm by assigning the optimum number of clusters and cluster centroids using the number of flames and flame values obtained in MFO. It uses color moments, HSV color histogram, color correlogram, GLCM, wavelet transform, dominant color, and region-based descriptors as feature vectors. The experiments are tested on Corel 1K dataset, and it shows competent results when compared with other retrieval techniques.
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References
Afifi AJ, Ashour WM (2012) Content-based image retrieval using invariant color and texture features. In: Int Conf on Digital Image Computing Techniques and Applications (DICTA). IEEE, Fremantle WA, pp 1–6 http://hdl.handle.net/20.500.12358/24456
Aksoy S, Haralick RM (2001) Feature normalization and likelihood-based similarity measures for image retrieval. Pattern Recogn Lett:563–582. https://doi.org/10.1016/S0167-8655(00)00112-4
Annrose J, Seldev CC (2016) Content based image retrieval using query based feature reduction with k-means cluster index. Asian J Res Soc Sci Humanit 6:852–872. https://doi.org/10.5958/2249-7315.2016.01334.4
Annrose J, CC CS (2018) An efficient image retrieval system with structured query based feature selection and filtering initial level relevant images using range query. Optik 157:1053–1064. https://doi.org/10.1016/j.ijleo.2017.11.179
Arai K, Rahmad C (2012) Wavelet based image retrieval method. Int J Adv Comput Sci Appl 3:6–11. https://doi.org/10.14569/IJACSA.2012.030402
Caron M, Bojanowski P, Joulin A, and Douze M (2019) Deep clustering for unsupervised learning of visual features. Computer Vision and Pattern Recognition, 1-30. https://arxiv.org/abs/1807.05520
Chen Y, James ZW, Krovetz R (2005) CLUE: cluster-based retrieval of images by unsupervised learning. IEEE Trans Image Process 14:1187–1201. https://doi.org/10.1109/tip.2005.849770
Cheung Y (2003) K-means: a new generalized k-means clustering algorithm. Pattern Recogn Lett 24:2883–2893. https://doi.org/10.1016/S0167-8655(03)00146-6
Chuen L, Chen RT, Chan YK (2009) A smart content-based image retrieval system based on color and texture feature. Image Vis Comput 27:658–665. https://doi.org/10.1016/j.imavis.2008.07.004
Dash JK, Mukhopadhyay S, Gupta RD (2015) Content-based image retrieval using fuzzy class membership and rules based on classifier confidence. IET Image Process 9:836–848. https://doi.org/10.1049/iet-ipr.2014.0299
Datta R, Li J & Wang JZ (2005) Content-based image retrieval - approaches and trends of the new age. MIR '05 Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval: 253-262. https://doi.org/10.1145/1101826.1101866
ElAlami ME (2011a) A novel image retrieval model based on the most relevant features. Knowl-Based Syst 24:23–32. https://doi.org/10.1016/j.knosys.2010.06.001
ElAlami ME (2011b) Supporting image retrieval framework with rule base system. Knowl-Based Syst 24:331–340. https://doi.org/10.1016/j.knosys.2010.10.005
Faloutsos C, Barber R, Flickner M, Hafner J, Niblack W, Petkovic D, Equitz W (1994) Efficient and effective querying by image content. J Intell Inf Syst 3:231–262. https://doi.org/10.1007/BF00962238
Gupta A, Jain R (1997) Visual information retrieval. Commun ACM 40:70–79. https://doi.org/10.1145/253769.253798
Huang PW, Dai SK (2003) Image retrieval by texture similarity. Pattern Recogn 36:665–679. https://doi.org/10.1016/S0031-3203(02)00083-3
Jhanwar N, Chaudhuri S, Seetharaman G, Zavidovique B (2004) Content based image retrieval using motif co-occurrence matrix. Image Vis Comput 22:1211–1220. https://doi.org/10.1016/j.imavis.2004.03.026
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. NIPS'12: Proceedings of the 25th International Conference on Neural Information Processing Systems 1:1097–1105. https://doi.org/10.5555/2999134.2999257
Liua Y, Zhanga D, Lua G, Mab W (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40:262–282. https://doi.org/10.1016/j.patcog.2006.04.045
Ma WY, Manjunath B (1997) Netra: a toolbox for navigating large image databases. Proceedings of the IEEE International Conference on Image Processing 568–571. https://doi.org/10.1007/s005300050121
Marjani M, Nasaruddin F, Gani A, Karim A, Hashem IAT, Siddiqa A (2017) Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5:5247–5261. https://doi.org/10.1109/ACCESS.2017.2689040
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006
Nene SA, Nayar SK, Murase H (n.d.) Columbia Object Image Library (COIL-100). Center for Research on Intelligent Systems at the Department of Computer Science, Columbia University
Otávio AB, Valle PE, Torre RS (2012) Comparative study of global color and texture descriptors for web image retrieval. J Vis Commun Image Represent 23:359–380. https://doi.org/10.1016/j.jvcir.2011.11.002
Pentland A, Picard RW, Scaroff S (1996) Photobook: content-based manipulation for image databases. Int J Comput Vis 18:233–254. https://doi.org/10.1007/BF00123143
Rao MB, Rao BP, Govardhan A (2011) CTDCIRS: content based image retrieval system based on dominant color and texture features. Int J Comput Appl 18(6):40–46. https://doi.org/10.5120/2285-2961
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet Large Scale Visual Recognition Challenge. IJCV https://arxiv.org/abs/1409.0575
Smith JR, Chang SF (1996) VisualSeek: a fully automatic content based query system. Proceedings of the Fourth ACM International Conference on Multimedia. 87–98. https://www.ee.columbia.edu/ln/dvmm/publications/96/smith96f.pdf
Su WT, Chen JC, Lien JJJ (2010) Region-based image retrieval system with heuristic pre-clustering relevance feedback. Expert Syst Appl 37:4984–4998. https://doi.org/10.1016/j.eswa.2009.12.015
Wan J, Wang D, Hoi SCH, Wu P, Zhu J, Zhang Y, Li J (2014) Deep learning for content-based image retrieval: a comprehensive study. Proc of the ACM Int Conf on Multimedia. https://doi.org/10.1145/2647868.2654948
Wang JZ, Li J, Wiederhold G (2001) SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23:947–963. https://doi.org/10.1109/34.955109
Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans on Neural Netw 16(3):645–677. https://doi.org/10.1109/TNN.2005.845141
Yang J, Parikh D, Batra D (2016) Joint unsupervised learning of deep representations and image clusters. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1-19. https://arxiv.org/abs/1604.03628
Yildizer E, Balci AM, Jarada TN, Alhajj R (2012) Integrating wavelets with clustering and indexing for effective content-based image retrieval. Knowl-Based Syst 31:55–66. https://doi.org/10.1016/j.knosys.2012.01.013
Younus ZS, Mohamad D, Saba T, Alkawaz MH, Rehman A, Al-Rodhaan Z, Al-Dhelaan A (2015) Content-based image retrieval using PSO and k-means clustering algorithm. Arab J Geosci 8:6211–6224. https://doi.org/10.1007/s12517-014-1584-7
Yue J, Li Z, Liu L, Fu Z (2011) Content-based image retrieval using color and texture fused features. Math Comput Model 54:1121–1127. https://doi.org/10.1016/j.mcm.2010.11.044
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Joseph, A., Rex, E.S., Christopher, S. et al. Content-based image retrieval using hybrid k-means moth flame optimization algorithm. Arab J Geosci 14, 687 (2021). https://doi.org/10.1007/s12517-021-06990-y
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DOI: https://doi.org/10.1007/s12517-021-06990-y